From b9f61390e9cf7f4b8b809cba56392d1f7b3ef6e6 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Tue, 8 Nov 2022 18:08:46 -0800 Subject: [PATCH] add text2text generation (#93) fixes issue #90 --- examples/huggingface_hub.ipynb | 20 ++------ langchain/llms/huggingface_hub.py | 49 ++++++++++--------- .../llms/test_huggingface_hub.py | 15 ++++-- 3 files changed, 39 insertions(+), 45 deletions(-) diff --git a/examples/huggingface_hub.ipynb b/examples/huggingface_hub.ipynb index e86910d41dc..06245be9a79 100644 --- a/examples/huggingface_hub.ipynb +++ b/examples/huggingface_hub.ipynb @@ -10,21 +10,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - "Justin Beiber was born in New York City on July 1, 1967. He was the son of the late John Beiber and his wife, Mary.\n", - "\n", - "Justin was raised in a small town in the Bronx, New York. He attended the University of New York at Buffalo, where he majored in English.\n", - "\n", - "Justin was a member of the New York Giants from 1967 to 1969. He was a member of the New York Giants from 1969 to 1971.\n", - "\n", - "Justin was a member of the New York Giants from 1971 to 1972. He was a member of the New York Giants from 1972 to 1974.\n", - "\n", - "Justin was a member of the New York Giants from 1974 to 1975. He was a member of the New York Giants from 1975 to 1977.\n", - "\n", - "Justin was a member of the New York Giants from 1977 to 1978. He was a member of the New York Giants from 1978 to 1979.\n", - "\n", - "Justin was a member of the New York Giants from 1979 to\n" + "The Seattle Seahawks won the Super Bowl in 2010. Justin Beiber was born in 2010. The\n" ] } ], @@ -35,7 +21,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "prompt = Prompt(template=template, input_variables=[\"question\"])\n", - "llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"gpt2\", temperature=1e-10))\n", + "llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1e-10}))\n", "\n", "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n", "\n", @@ -67,7 +53,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.7" } }, "nbformat": 4, diff --git a/langchain/llms/huggingface_hub.py b/langchain/llms/huggingface_hub.py index 349441558c4..fc83586eef5 100644 --- a/langchain/llms/huggingface_hub.py +++ b/langchain/llms/huggingface_hub.py @@ -1,6 +1,6 @@ """Wrapper around HuggingFace APIs.""" import os -from typing import Any, Dict, List, Mapping, Optional +from typing import Any, Dict, List, Optional from pydantic import BaseModel, Extra, root_validator @@ -8,6 +8,7 @@ from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens DEFAULT_REPO_ID = "gpt2" +VALID_TASKS = ("text2text-generation", "text-generation") class HuggingFaceHub(BaseModel, LLM): @@ -29,14 +30,10 @@ class HuggingFaceHub(BaseModel, LLM): client: Any #: :meta private: repo_id: str = DEFAULT_REPO_ID """Model name to use.""" - temperature: float = 0.7 - """What sampling temperature to use.""" - max_new_tokens: int = 200 - """The maximum number of tokens to generate in the completion.""" - top_p: int = 1 - """Total probability mass of tokens to consider at each step.""" - num_return_sequences: int = 1 - """How many completions to generate for each prompt.""" + task: Optional[str] = None + """Task to call the model with. Should be a task that returns `generated_text`.""" + model_kwargs: Optional[dict] = None + """Key word arguments to pass to the model.""" huggingfacehub_api_token: Optional[str] = os.environ.get("HUGGINGFACEHUB_API_TOKEN") @@ -49,7 +46,6 @@ class HuggingFaceHub(BaseModel, LLM): def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = values.get("huggingfacehub_api_token") - if huggingfacehub_api_token is None or huggingfacehub_api_token == "": raise ValueError( "Did not find HuggingFace API token, please add an environment variable" @@ -60,11 +56,17 @@ class HuggingFaceHub(BaseModel, LLM): from huggingface_hub.inference_api import InferenceApi repo_id = values.get("repo_id", DEFAULT_REPO_ID) - values["client"] = InferenceApi( + client = InferenceApi( repo_id=repo_id, token=huggingfacehub_api_token, - task="text-generation", + task=values.get("task"), ) + if client.task not in VALID_TASKS: + raise ValueError( + f"Got invalid task {client.task}, " + f"currently only {VALID_TASKS} are supported" + ) + values["client"] = client except ImportError: raise ValueError( "Could not import huggingface_hub python package. " @@ -72,16 +74,6 @@ class HuggingFaceHub(BaseModel, LLM): ) return values - @property - def _default_params(self) -> Mapping[str, Any]: - """Get the default parameters for calling HuggingFace Hub API.""" - return { - "temperature": self.temperature, - "max_new_tokens": self.max_new_tokens, - "top_p": self.top_p, - "num_return_sequences": self.num_return_sequences, - } - def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str: """Call out to HuggingFace Hub's inference endpoint. @@ -97,10 +89,19 @@ class HuggingFaceHub(BaseModel, LLM): response = hf("Tell me a joke.") """ - response = self.client(inputs=prompt, params=self._default_params) + response = self.client(inputs=prompt, params=self.model_kwargs) if "error" in response: raise ValueError(f"Error raised by inference API: {response['error']}") - text = response[0]["generated_text"][len(prompt) :] + if self.client.task == "text-generation": + # Text generation return includes the starter text. + text = response[0]["generated_text"][len(prompt) :] + elif self.client.task == "text2text-generation": + text = response[0]["generated_text"] + else: + raise ValueError( + f"Got invalid task {self.client.task}, " + f"currently only {VALID_TASKS} are supported" + ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. diff --git a/tests/integration_tests/llms/test_huggingface_hub.py b/tests/integration_tests/llms/test_huggingface_hub.py index 7583b59c257..aa181a87c85 100644 --- a/tests/integration_tests/llms/test_huggingface_hub.py +++ b/tests/integration_tests/llms/test_huggingface_hub.py @@ -5,15 +5,22 @@ import pytest from langchain.llms.huggingface_hub import HuggingFaceHub -def test_huggingface_call() -> None: - """Test valid call to HuggingFace.""" - llm = HuggingFaceHub(max_new_tokens=10) +def test_huggingface_text_generation() -> None: + """Test valid call to HuggingFace text generation model.""" + llm = HuggingFaceHub(repo_id="gpt2", model_kwargs={"max_new_tokens": 10}) output = llm("Say foo:") assert isinstance(output, str) +def test_huggingface_text2text_generation() -> None: + """Test valid call to HuggingFace text2text model.""" + llm = HuggingFaceHub(repo_id="google/flan-t5-xl") + output = llm("The capital of New York is") + assert output == "Albany" + + def test_huggingface_call_error() -> None: """Test valid call to HuggingFace that errors.""" - llm = HuggingFaceHub(max_new_tokens=-1) + llm = HuggingFaceHub(model_kwargs={"max_new_tokens": -1}) with pytest.raises(ValueError): llm("Say foo:")